{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import codecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath=r'C:\\OneDrive\\Stock' + r'\\小牛1号.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\OneDrive\\\\Stock\\\\小牛1号.txt'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "with codecs.open(filepath,'r',encoding='utf8') as fp:\n",
    "    js_data = json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "index=0\n",
    "name = js_data[index]['name']\n",
    "netvalue_list = js_data[index]['list']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " {'date': '2020-03-20', 'value': 4.344, 'percent': 3.344},\n",
       " {'date': '2020-03-27', 'value': 4.473, 'percent': 3.473},\n",
       " {'date': '2020-04-03', 'value': 4.492, 'percent': 3.492},\n",
       " {'date': '2020-04-10', 'value': 4.57, 'percent': 3.57},\n",
       " {'date': '2020-04-17', 'value': 4.659, 'percent': 3.659},\n",
       " {'date': '2020-04-24', 'value': 4.662, 'percent': 3.662},\n",
       " {'date': '2020-04-30', 'value': 4.753, 'percent': 3.753},\n",
       " {'date': '2020-05-08', 'value': 4.835, 'percent': 3.835},\n",
       " {'date': '2020-05-15', 'value': 4.933, 'percent': 3.933},\n",
       " {'date': '2020-05-22', 'value': 4.802, 'percent': 3.802},\n",
       " {'date': '2020-05-29', 'value': 4.966, 'percent': 3.966},\n",
       " {'date': '2020-06-05', 'value': 5.089, 'percent': 4.089},\n",
       " {'date': '2020-06-12', 'value': 5.19, 'percent': 4.19},\n",
       " {'date': '2020-06-19', 'value': 5.342, 'percent': 4.342},\n",
       " {'date': '2020-06-24', 'value': 5.449, 'percent': 4.449},\n",
       " {'date': '2020-07-03', 'value': 5.803, 'percent': 4.803},\n",
       " {'date': '2020-07-10', 'value': 6.195, 'percent': 5.195},\n",
       " {'date': '2020-07-17', 'value': 5.988, 'percent': 4.988},\n",
       " {'date': '2020-07-24', 'value': 6.133, 'percent': 5.133},\n",
       " {'date': '2020-07-31', 'value': 6.328, 'percent': 5.328},\n",
       " {'date': '2020-08-07', 'value': 6.47, 'percent': 5.47},\n",
       " {'date': '2020-08-14', 'value': 6.409, 'percent': 5.409},\n",
       " {'date': '2020-08-21', 'value': 6.606, 'percent': 5.606},\n",
       " {'date': '2020-08-28', 'value': 6.769, 'percent': 5.769},\n",
       " {'date': '2020-09-04', 'value': 6.671, 'percent': 5.671},\n",
       " {'date': '2020-09-11', 'value': 6.518, 'percent': 5.518},\n",
       " {'date': '2020-09-18', 'value': 6.594, 'percent': 5.594},\n",
       " {'date': '2020-09-25', 'value': 6.426, 'percent': 5.426},\n",
       " {'date': '2020-09-30', 'value': 6.481, 'percent': 5.481},\n",
       " {'date': '2020-10-09', 'value': 6.618, 'percent': 5.618},\n",
       " {'date': '2020-10-16', 'value': 6.67, 'percent': 5.67},\n",
       " {'date': '2020-10-23', 'value': 6.58, 'percent': 5.58},\n",
       " {'date': '2020-10-30', 'value': 6.581, 'percent': 5.581},\n",
       " {'date': '2020-11-06', 'value': 6.91, 'percent': 5.91},\n",
       " {'date': '2020-11-13', 'value': 6.888, 'percent': 5.888},\n",
       " {'date': '2020-11-20', 'value': 6.992, 'percent': 5.992},\n",
       " {'date': '2020-11-27', 'value': 6.961, 'percent': 5.961},\n",
       " {'date': '2020-12-04', 'value': 6.991, 'percent': 5.991}]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netvalue_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(netvalue_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-05-08</td>\n",
       "      <td>1.006</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-15</td>\n",
       "      <td>1.014</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  value  percent\n",
       "0  2015-04-21  1.000    0.000\n",
       "1  2015-04-24  1.000    0.000\n",
       "2  2015-04-30  1.000    0.000\n",
       "3  2015-05-08  1.006    0.006\n",
       "4  2015-05-15  1.014    0.014"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.048073</td>\n",
       "      <td>2.048073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.509813</td>\n",
       "      <td>1.509813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.601750</td>\n",
       "      <td>0.601750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.075000</td>\n",
       "      <td>2.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.866000</td>\n",
       "      <td>2.866000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.992000</td>\n",
       "      <td>5.992000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            value     percent\n",
       "count  288.000000  288.000000\n",
       "mean     3.048073    2.048073\n",
       "std      1.509813    1.509813\n",
       "min      1.000000    0.000000\n",
       "25%      1.601750    0.601750\n",
       "50%      3.075000    2.075000\n",
       "75%      3.866000    2.866000\n",
       "max      6.992000    5.992000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2020-07-24</td>\n",
       "      <td>6.133</td>\n",
       "      <td>5.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2020-07-31</td>\n",
       "      <td>6.328</td>\n",
       "      <td>5.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2020-08-07</td>\n",
       "      <td>6.470</td>\n",
       "      <td>5.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>6.409</td>\n",
       "      <td>5.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2020-08-21</td>\n",
       "      <td>6.606</td>\n",
       "      <td>5.606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>6.769</td>\n",
       "      <td>5.769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>6.671</td>\n",
       "      <td>5.671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>2020-09-11</td>\n",
       "      <td>6.518</td>\n",
       "      <td>5.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>6.594</td>\n",
       "      <td>5.594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2020-09-25</td>\n",
       "      <td>6.426</td>\n",
       "      <td>5.426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2020-09-30</td>\n",
       "      <td>6.481</td>\n",
       "      <td>5.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2020-10-09</td>\n",
       "      <td>6.618</td>\n",
       "      <td>5.618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>6.670</td>\n",
       "      <td>5.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2020-10-23</td>\n",
       "      <td>6.580</td>\n",
       "      <td>5.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>6.581</td>\n",
       "      <td>5.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>6.910</td>\n",
       "      <td>5.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>6.888</td>\n",
       "      <td>5.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>6.992</td>\n",
       "      <td>5.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>6.961</td>\n",
       "      <td>5.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>6.991</td>\n",
       "      <td>5.991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  value  percent\n",
       "268  2020-07-24  6.133    5.133\n",
       "269  2020-07-31  6.328    5.328\n",
       "270  2020-08-07  6.470    5.470\n",
       "271  2020-08-14  6.409    5.409\n",
       "272  2020-08-21  6.606    5.606\n",
       "273  2020-08-28  6.769    5.769\n",
       "274  2020-09-04  6.671    5.671\n",
       "275  2020-09-11  6.518    5.518\n",
       "276  2020-09-18  6.594    5.594\n",
       "277  2020-09-25  6.426    5.426\n",
       "278  2020-09-30  6.481    5.481\n",
       "279  2020-10-09  6.618    5.618\n",
       "280  2020-10-16  6.670    5.670\n",
       "281  2020-10-23  6.580    5.580\n",
       "282  2020-10-30  6.581    5.581\n",
       "283  2020-11-06  6.910    5.910\n",
       "284  2020-11-13  6.888    5.888\n",
       "285  2020-11-20  6.992    5.992\n",
       "286  2020-11-27  6.961    5.961\n",
       "287  2020-12-04  6.991    5.991"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date, value, percent]\n",
       "Index: []"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['percent']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 288 entries, 0 to 287\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   date     288 non-null    object \n",
      " 1   value    288 non-null    float64\n",
      " 2   percent  288 non-null    float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a1cfa0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['percent'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pct']=df['value'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>value</th>\n",
       "      <th>percent</th>\n",
       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-05-08</td>\n",
       "      <td>1.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-15</td>\n",
       "      <td>1.014</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  value  percent       pct\n",
       "0  2015-04-21  1.000    0.000       NaN\n",
       "1  2015-04-24  1.000    0.000       NaN\n",
       "2  2015-04-30  1.000    0.000       NaN\n",
       "3  2015-05-08  1.006    0.006       inf\n",
       "4  2015-05-15  1.014    0.014  1.333333"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>value</th>\n",
       "      <th>percent</th>\n",
       "      <th>ptc</th>\n",
       "      <th>pct</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>6.910</td>\n",
       "      <td>5.910</td>\n",
       "      <td>0.058950</td>\n",
       "      <td>0.058950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>6.888</td>\n",
       "      <td>5.888</td>\n",
       "      <td>-0.003723</td>\n",
       "      <td>-0.003723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>6.992</td>\n",
       "      <td>5.992</td>\n",
       "      <td>0.017663</td>\n",
       "      <td>0.017663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>6.961</td>\n",
       "      <td>5.961</td>\n",
       "      <td>-0.005174</td>\n",
       "      <td>-0.005174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>6.991</td>\n",
       "      <td>5.991</td>\n",
       "      <td>0.005033</td>\n",
       "      <td>0.005033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  value  percent       ptc       pct\n",
       "283  2020-11-06  6.910    5.910  0.058950  0.058950\n",
       "284  2020-11-13  6.888    5.888 -0.003723 -0.003723\n",
       "285  2020-11-20  6.992    5.992  0.017663  0.017663\n",
       "286  2020-11-27  6.961    5.961 -0.005174 -0.005174\n",
       "287  2020-12-04  6.991    5.991  0.005033  0.005033"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pct']=df['pct']*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('ptc',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>value</th>\n",
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       "      <th>pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-05-08</td>\n",
       "      <td>1.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-15</td>\n",
       "      <td>1.014</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  value  percent       pct\n",
       "0  2015-04-21  1.000    0.000       NaN\n",
       "1  2015-04-24  1.000    0.000       NaN\n",
       "2  2015-04-30  1.000    0.000       NaN\n",
       "3  2015-05-08  1.006    0.006       inf\n",
       "4  2015-05-15  1.014    0.014  1.333333"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2015-04-21</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-05-08</td>\n",
       "      <td>1.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>inf</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-15</td>\n",
       "      <td>1.014</td>\n",
       "      <td>0.014</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-05-22</td>\n",
       "      <td>1.046</td>\n",
       "      <td>0.046</td>\n",
       "      <td>2.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-05-29</td>\n",
       "      <td>1.095</td>\n",
       "      <td>0.095</td>\n",
       "      <td>1.065217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-06-05</td>\n",
       "      <td>1.136</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.431579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-06-12</td>\n",
       "      <td>1.130</td>\n",
       "      <td>0.130</td>\n",
       "      <td>-0.044118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-06-19</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.099</td>\n",
       "      <td>-0.238462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  value  percent       ptc\n",
       "0  2015-04-21  1.000    0.000       NaN\n",
       "1  2015-04-24  1.000    0.000       NaN\n",
       "2  2015-04-30  1.000    0.000       NaN\n",
       "3  2015-05-08  1.006    0.006       inf\n",
       "4  2015-05-15  1.014    0.014  1.333333\n",
       "5  2015-05-22  1.046    0.046  2.285714\n",
       "6  2015-05-29  1.095    0.095  1.065217\n",
       "7  2015-06-05  1.136    0.136  0.431579\n",
       "8  2015-06-12  1.130    0.130 -0.044118\n",
       "9  2015-06-19  1.099    0.099 -0.238462"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>283</th>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>6.910</td>\n",
       "      <td>5.910</td>\n",
       "      <td>0.058950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>6.888</td>\n",
       "      <td>5.888</td>\n",
       "      <td>-0.003723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>6.992</td>\n",
       "      <td>5.992</td>\n",
       "      <td>0.017663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>6.961</td>\n",
       "      <td>5.961</td>\n",
       "      <td>-0.005174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>6.991</td>\n",
       "      <td>5.991</td>\n",
       "      <td>0.005033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  value  percent       ptc\n",
       "283  2020-11-06  6.910    5.910  0.058950\n",
       "284  2020-11-13  6.888    5.888 -0.003723\n",
       "285  2020-11-20  6.992    5.992  0.017663\n",
       "286  2020-11-27  6.961    5.961 -0.005174\n",
       "287  2020-12-04  6.991    5.991  0.005033"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>288.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.048073</td>\n",
       "      <td>2.048073</td>\n",
       "      <td>0.724861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.509813</td>\n",
       "      <td>1.509813</td>\n",
       "      <td>3.088422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-13.169074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.601750</td>\n",
       "      <td>0.601750</td>\n",
       "      <td>-1.222082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.075000</td>\n",
       "      <td>2.075000</td>\n",
       "      <td>0.817626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.866000</td>\n",
       "      <td>2.866000</td>\n",
       "      <td>2.607213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.992000</td>\n",
       "      <td>5.992000</td>\n",
       "      <td>12.491373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            value     percent         pct\n",
       "count  288.000000  288.000000  288.000000\n",
       "mean     3.048073    2.048073    0.724861\n",
       "std      1.509813    1.509813    3.088422\n",
       "min      1.000000    0.000000  -13.169074\n",
       "25%      1.601750    0.601750   -1.222082\n",
       "50%      3.075000    2.075000    0.817626\n",
       "75%      3.866000    2.866000    2.607213\n",
       "max      6.992000    5.992000   12.491373"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.491373360938574"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pct'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-05-08</td>\n",
       "      <td>1.006</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-05-15</td>\n",
       "      <td>1.014</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.795229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>2020-11-06</td>\n",
       "      <td>6.910</td>\n",
       "      <td>5.910</td>\n",
       "      <td>4.999240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>6.888</td>\n",
       "      <td>5.888</td>\n",
       "      <td>-0.318379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>6.992</td>\n",
       "      <td>5.992</td>\n",
       "      <td>1.509872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>6.961</td>\n",
       "      <td>5.961</td>\n",
       "      <td>-0.443364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>6.991</td>\n",
       "      <td>5.991</td>\n",
       "      <td>0.430973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  value  percent       pct\n",
       "0    2015-04-21  1.000    0.000  0.000000\n",
       "1    2015-04-24  1.000    0.000  0.000000\n",
       "2    2015-04-30  1.000    0.000  0.000000\n",
       "3    2015-05-08  1.006    0.006  0.600000\n",
       "4    2015-05-15  1.014    0.014  0.795229\n",
       "..          ...    ...      ...       ...\n",
       "283  2020-11-06  6.910    5.910  4.999240\n",
       "284  2020-11-13  6.888    5.888 -0.318379\n",
       "285  2020-11-20  6.992    5.992  1.509872\n",
       "286  2020-11-27  6.961    5.961 -0.443364\n",
       "287  2020-12-04  6.991    5.991  0.430973\n",
       "\n",
       "[288 rows x 4 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace([np.inf, -np.inf], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005032712632108599"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(5.991-5.961)/5.961"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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